'''
_base_ = [
     '../_base_/datasets/stare.py',
    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
# model settings
norm_cfg = dict(type='SyncBN', eps=0.001, requires_grad=True)
model = dict(
    type='EncoderDecoder',
    pretrained=None,
    backbone=dict(
        type='UNet',
        in_channels=3,
        base_channels=64,
        num_stages=5,
        strides=(1, 1, 1, 1, 1),
        enc_num_convs=(2, 2, 2, 2, 2),
        dec_num_convs=(2, 2, 2, 2),
        downsamples=(True, True, True, True),
        enc_dilations=(1, 1, 1, 1, 1),
        dec_dilations=(1, 1, 1, 1),
        with_cp=False,
        conv_cfg=None,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        act_cfg=dict(type='ReLU'),
        upsample_cfg=dict(type='InterpConv'),
        norm_eval=False),
    decode_head=dict(
        type='FCNHead',
        in_channels=64,
        in_index=4,
        channels=64,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        num_classes=2,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        align_corners=False,
        loss_decode=[
            dict(
                type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
            dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
        ]),
    auxiliary_head=dict(
        type='FCNHead',
        in_channels=128,
        in_index=3,
        channels=64,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        num_classes=2,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
    train_cfg=dict(),
    test_cfg=dict(mode='slide', crop_size=(128, 128), stride=(85, 85)))

'''
_base_ = [
     '../_base_/datasets/stare.py',
    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
# model settings
norm_cfg = dict(type='SyncBN', eps=0.001, requires_grad=True)
model = dict(
    type='EncoderDecoder',
    pretrained=None,
    backbone=dict(
        type='MobileNetV3',
        arch='large',
        out_indices=(0,2,5,11,14),
        #out_indices=(1,3,5,11,16)
        norm_cfg=norm_cfg),
    decode_head=dict(
        type='FCNHead',
        in_channels=160,
        in_index=4,
        channels=64,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        num_classes=2,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        align_corners=False,
        loss_decode=[
            dict(
                type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
            dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
        ]),
    auxiliary_head=dict(
        type='FCNHead',
        in_channels=112,
        in_index=3,
        channels=64,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        num_classes=2,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
    train_cfg=dict(),
    test_cfg=dict(mode='slide', crop_size=(128, 128), stride=(85, 85)))
